Human Gait Recognition via Sparse Discriminant Projection Learning

被引:93
|
作者
Lai, Zhihui [1 ,2 ]
Xu, Yong [1 ,2 ]
Jin, Zhong [3 ]
Zhang, David [4 ]
机构
[1] Harbin Inst Technol, Shenzhen Grad Sch, Biocomp Res Ctr, Shenzhen 518055, Peoples R China
[2] Shenzhen Univ, Coll Comp Sci & Software Engn, Shenzhen 518060, Peoples R China
[3] Nanjing Univ Sci & Technol, Sch Comp Sci, Nanjing 210094, Jiangsu, Peoples R China
[4] Hong Kong Polytech Univ, Dept Comp, Biometr Res Ctr, Kowloon, Hong Kong, Peoples R China
关键词
Feature extraction; gait recognition; linear discriminant analysis (LDA); sparse regression; ANGLE; EIGENFACES; REGRESSION; SELECTION; FEATURES;
D O I
10.1109/TCSVT.2014.2305495
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
As an important biometric feature, human gait has great potential in video-surveillance-based applications. In this paper, we focus on the matrix representation-based human gait recognition and propose a novel discriminant subspace learning method called sparse bilinear discriminant analysis (SBDA). SBDA extends the recently proposed matrix-representation-based discriminant analysis methods to sparse cases. By introducing the L-1 and L-2 norms into the objective function of SBDA, two interrelated sparse discriminant subspaces can be obtained for gait feature extraction. Since the optimization problem has no closed-form solutions, an iterative method is designed to compute the optimal sparse subspace using the L-1 and L-2 norms sparse regression. Theoretical analyses reveal the close relationship between SBDA and previous matrix-representation-based discriminant analysis methods. Since each nonzero element in each subspace is selected from the most important variables/factors, SBDA is potential to perform equivalent to or even better than the state-of-the-art subspace learning methods in gait recognition. Moreover, using the strategy of SBDA plus linear discriminant analysis (LDA), we can further improve the performance. A set of experiments on the standard USF HumanID and CASIA gait databases demonstrate that the proposed SBDA and SBDA + LDA can obtain competitive performance.
引用
收藏
页码:1651 / 1662
页数:12
相关论文
共 50 条
  • [21] Combining weighted adaptive CS-LBP and local linear discriminant projection for gait recognition
    Shanwen Zhang
    Liqing Zhang
    Multimedia Tools and Applications, 2018, 77 : 12331 - 12347
  • [22] Combining weighted adaptive CS-LBP and local linear discriminant projection for gait recognition
    Zhang, Shanwen
    Zhang, Liqing
    MULTIMEDIA TOOLS AND APPLICATIONS, 2018, 77 (10) : 12331 - 12347
  • [23] Gait Recognition via Disentangled Representation Learning
    Zhang, Ziyuan
    Tran, Luan
    Yin, Xi
    Atoum, Yousef
    Liu, Xiaoming
    Wan, Jian
    Wang, Nanxin
    2019 IEEE/CVF CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR 2019), 2019, : 4705 - 4714
  • [24] Neighborhood Discriminant Projection for face recognition
    You, Qubo
    Zheng, Nanning
    Du, Shaoyi
    Wu, Yang
    18TH INTERNATIONAL CONFERENCE ON PATTERN RECOGNITION, VOL 2, PROCEEDINGS, 2006, : 532 - +
  • [25] Manifold Discriminant Projection for face Recognition
    Chen, Caikou
    Yu, Yiming
    Hou, Yu
    2011 AASRI CONFERENCE ON APPLIED INFORMATION TECHNOLOGY (AASRI-AIT 2011), VOL 1, 2011, : 343 - 346
  • [26] Neighborhood discriminant projection for face recognition
    You, Qubo
    Zheng, Nanning
    Du, Shaoyi
    Wu, Yang
    PATTERN RECOGNITION LETTERS, 2007, 28 (10) : 1156 - 1163
  • [27] Research On Algorithm Of Human Gait Recognition Based On Sparse Representation
    Guan, Y. D.
    Zhu, R. F.
    Feng, J. Y.
    Du, K.
    Zhang, X. R.
    PROCEEDINGS OF 2016 SIXTH INTERNATIONAL CONFERENCE ON INSTRUMENTATION & MEASUREMENT, COMPUTER, COMMUNICATION AND CONTROL (IMCCC 2016), 2016, : 405 - 410
  • [28] Emotion recognition in the wild via sparse transductive transfer linear discriminant analysis
    Yuan Zong
    Wenming Zheng
    Xiaohua Huang
    Keyu Yan
    Jingwei Yan
    Tong Zhang
    Journal on Multimodal User Interfaces, 2016, 10 : 163 - 172
  • [29] Emotion recognition in the wild via sparse transductive transfer linear discriminant analysis
    Zong, Yuan
    Zheng, Wenming
    Huang, Xiaohua
    Yan, Keyu
    Yan, Jingwei
    Zhang, Tong
    JOURNAL ON MULTIMODAL USER INTERFACES, 2016, 10 (02) : 163 - 172
  • [30] Discriminant Manifold Learning via Sparse Coding for Robust Feature Extraction
    Pang, Meng
    Wang, Binghui
    Cheung, Yiu-Ming
    Lin, Chuang
    IEEE ACCESS, 2017, 5 : 13978 - 13991